Power Your Retail Product Recommendation Engine with CIM

Both eCommerce and retail companies struggle to track changes in customers’ preferences and constantly update themselves to serve accurate recommendations.

The solution to such a problem is the use of AI-driven product recommendation engines.

Both retail and eCommerce businesses adopt ML-based personalized product recommendations to select items to be displayed across the web or social media, and email.   

How Do Product Recommendation Engines Work?

Recommendations and predictive suggestions related to special deals customized for individual audiences are produced by product recommendation engines.

They can go through the data, and make use of results to produce the right and customized audience profiles. 

The recommendation engine will consider such audience profiles to produce audience-oriented products or content without neglecting audience interests.

As a result, audiences might receive follow-up emails from their favorite brands based on their latest site interaction or purchase history.

The engine can inspect the below-mentioned kinds of consumer data:

  1. Browsing history
  2. Present buying behavior
  3. Interests
  4. Previous purchases
  5. Wish lists
  6. Recently searched products
  7. Shopping carts
  8. Opinions

It can smartly understand the intent of consumers and add the suggested items in website searches, on ads displayed on web pages, in marketing brochures, and on an app. 

The engine is based on sophisticated algorithms. Such algorithms consider large quantities of data related to consumers such as search behavior, interests, and the history of recent purchases. 

These algorithms allow set procedures to automatically produce relevant recommendations after looking at consumer data.

Later, the best recommendations were delivered to every individual by the system.

When there is fresh customer data available, the system implements that criteria and provides upgraded suggestions. 

A tailored product recommendation engine involves predefined rules set by the organization to filter and categorize products from an online store.

This process employs product data containing reviews, sales, and several views to discover well-liked products in numerous ways. 

The presentation of these outcomes is simple and similar to the order of products that are shown on the homepage or category pages of any website.

Later, that can be used to influence purchasers at each step of the visitor journey

Based on visitor-specific information like the visitor’s most viewed products, categories, and previous purchases, both eCommerce and retail product recommendation engines discover accurate recommendations for visitors.

These product recommendations use AI in conjunction with machine learning algorithms and natural language processing to fuel limitless features of the customer experience such as:

  1. Email campaigns
  2. Category, product, and add-to-cart pages
  3. Personalized social or display ads
  4. Home pages
  5. Discounts within search results

Product recommendations act as a platform to increase revenue for the business.

Different Types of Personalized Product Recommendations

You can find numerous types of personalized product recommendations and each type has its own specialties. You have to decide which type is most suitable for your business.

Let’s illustrate this with Amazon Prime:

Trending recommendations

These recommendations are judged based on social proof in a specific geographic region or popularity.

For instance, depending on the data collected from Google Analytics and social media. 

Personalized recommendations

The Amazon Prime recommender system makes predictions regarding what viewers will like to watch at present. 

Business-oriented recommendations

These are crucial for Amazon Prime for two reasons:

  1. They have invested more money in producing them and are available only on Amazon Prime
  2. When viewers watch content, Amazon Prime must pay the content owners. However, if Amazon Prime is the owner, they save a lot of money and boost their revenue.

Want to know how to boost your profits with an ML-based product recommendation engine?

What are the Three Types of Recommendation Engines?

eCommerce product recommendation engines vary depending on the particular data they gather and how they utilize it to discover the items they recommend to a client.

There are 3 major approaches:

Collaborative filtering

It examines information from various clients to forecast what products will meet the needs of individuals, and then offers highly powerful product suggestions. 

For instance, a consumer looking at a moisturizing cream on a personal care website may look at recommended products purchased by others who viewed the same item.

Similarly, they also view products consumers purchased along with the moisturizing cream, like a facial cleanser. 

A collaborative filtering system is the best choice for businesses that have access to a wide range of consumer data. 

This filtering system’s main drawback is that it is prone to making false predictions. It is possible to guess incorrectly that a customer will like a product only because others did. 

In collaborative filtering & content-based filtering recommendation systems, propensity models can be deployed within a CDP to build relevant recommendations if historical data are scarce.

Content-based filtering

It examines the preferences and buying behaviors of individual customers.

A content-based filtering system produces a special optional profile and provides recommendations keeping the personal interests of the customer in mind.

A visitor who’s frequently visiting a website will have an optional profile built on any of the below-mentioned data:

  1. Age
  2. Geolocation
  3. Social presence
  4. Browsing device
  5. Purchase history

The AI engine looks at all the aforementioned data to predict what the visitor might possibly wish to buy. 

Hybrid recommendation system

It provides mixed filtering options, especially collaborative and content-based.

In short, a hybrid recommendation system utilizes information from segments of related users and the previous preferences of an individual visitor. 

Hybrid systems normally run these inspections separately and later merge them to provide personalized online recommendations.

Why Do Businesses Prefer Personalized Product Recommendation Engines?

Recommender systems in machine learning can act as a link between shoppers preferences and data science.

According to 98% of marketing professionals, personalization strengthens client relationships and they have doubled their ROI using advanced personalization. 

Businesses prefer personalized product recommendation engines, as they provide an opportunity for businesses to use the behavioral data of clients to take their customer service strategies to higher levels and also generate possible ROI for their marketing campaigns.

Relevant recommendation engine technology can increase awareness of the products or brand and boost customer satisfaction in multiple ways.

Furthermore, a research report by Barilliance states that personalized product recommendation systems can generate up to 31% of eCommerce sales revenue.    

What are the Benefits of a Product Recommendation Engine?

Optimized inventory

Providing visitors with what they need helps a business avoid stocking up on unwanted products.

Here, personalized product recommendations come into play. It provides inclusive insight into what kinds of products are getting more impressions or clicks.

Based on this, eCommerce and retail businesses can decide to optimize their inventory. 

Saves time

The most time-consuming, and toughest task for entrepreneurs is manually setting up their store for product suggestions, cross-sells, and upsells.

In this case, the chances are high that customers might discover irrelevant product recommendations coming at them. They don’t waste time leaving the website. 

Businesses can overcome these troubles using AI-based product recommendation engines.

Businesses need to set up their merchandising rules once, and AI engines automatically produce personalized recommendations for customers.

Thus, there is plenty of time left for businesses to concentrate more on improving and managing operations. 

More conversions

If an organization expects more prospects to subscribe to their mailing list, it’s possible by embedding a personalized product into an opt-in form. 

After sending product catalog emails to subscribers, the possibility of making a purchase will increase. Isn’t it a good idea to expect higher sales? 

Greater user engagement

Trust is the foundation of greater customer engagement.

Users like to feel like the business understands them and suggesting the relevant items will assist nurture brand loyalty, encourage more website visits, and result in enhanced user satisfaction. 

Product recommendations for eCommerce play a key role in engaging users and minimizing their search efforts.

Product Recommendation Engines: Do All Personalized Recommendations Get Delivered?

No. Few recommendation engines provide non-personalized suggestions/recommendations via numerous sources of information and systems like collaborative filtering systems, business rules, and social proof.

Let’s see how it all works:

Collaborative recommendations

Customers who viewed those glass hand soap dispensers looked at these study trays.

Social proof

Products that have got more ratings, best seller ranks, and trending items are shown in the seen category.

Business rules

Marketers choose to display products from a category including best-selling products, products on sale, and complimentary items.

What are the Challenges with Product Recommendation Engines?

Even Though recommendation engines for personalization have become an important element of daily business operations, building powerful product recommendation systems creates crucial challenges.

Let’s explore such challenges in detail and how to overcome them:

Data scarcity:

Data scarcity is a frequently seen challenge in recommendation engines where there will be multiple shoppers and products, but only a few of them will connect. 

The best way to deal with data scarcity is to utilize matrix factorization concepts including SVD (Singular Value Decomposition) or NMF (Non-negative Matrix Factorization) to put disappeared values and create recommendations. 

Another way to solve this problem is to employ collaborative filtering systems like user-oriented or item-oriented methods to suggest products according to similar products or users. 

Cold start problem:

When there is not enough data for fresh users or products, the cold start problem arises.

For instance, a fresh shopper might have visited an eCommerce site and registered but there would be no purchase or browsing history to draw upon when making relevant recommendations. 

The most powerful way to solve the cold start problem is to employ content-oriented filtering algorithms.

This method suggests products according to their features like price, description, and category.

For instance, if a fresh user is looking for a mobile, the system can suggest mobiles with identical features to other mobiles the user has previously seen or bought.  


Diversity is crucial in recommendation engines as shoppers would like to find fresh and compelling products excluding the popular ones.

The problem with recommendation engines is that they may suggest only well-liked products resulting in inadequate diversity.

The best way to deal with diversity is to employ diversity metrics including novelty and entropy to calculate the diversity of recommendations.

Another way is to employ serendipity-oriented recommendations that suggest unpredicted products that match the needs of the shoppers’ interests.


Privacy has become a critical concern as product recommendation systems usually need access to shopper data like purchase and browsing histories to offer customized recommendations but shoppers may not show interest in sharing their data. 

The advanced way to solve this issue is to employ anonymization concepts like hashing and encryption to protect shopper data.

Another way is to employ dissimilar privacy, which inserts scattered noise into the recommendations to safeguard shopper data. 


Scalability is another challenge in product recommendation systems when huge databases contain millions of shoppers and products.

A product recommendation system should easily manage a huge quantity of data and produce recommendations immediately. 

The powerful solution to overcome this issue is to employ scattered computing frameworks like Apache Hadoop or Apache Spark to manage huge databases.

Another way is by caching predetermined suggestions and making them easily accessible.

For instance, a recommendation system for an eCommerce website may cache suggestions for repeatedly seen items or favored categories to boost the recommendation procedure.

Use Customer information management (CIM) for better Recommendations on your recommendation engine

How Do Product Recommendation Engines Help Your Retail Business?

In the retail and eCommerce sectors, personalized AI product recommendation engines increase conversions by supporting both up-selling and cross-selling activities.

They can decide whether down-selling or up-selling is the best approach for the business based on the situation.

The factor that decides which approach is optimal purely depends on an analysis of every individual’s intent. 

With the introduction of propensity modeling and predictive analytics, retail businesses can leverage insights gathered throughout the journey and customer sentiment to release customer intent.

Based on this analysis, they can personalize all interactions in a way that relates to the needs of customers.

Examples of Product Recommendation

Examples of product recommendation technology are illustrated below:


You might have observed various recommendations on every device, page, and channel while browsing Amazon for online shopping.

These are impressive and unique strategies to impress shoppers. 

Amazon offers recommendations by comparing related items, and combos, and encourages shoppers to sign in if they log out.

Thus, it utilizes recommendations as a secret weapon to change the users’ mindset and force them to take relevant actions.


It relies on data science, AI, and machine learning to suggest accurate recommendations for 100 million subscribers.

Netflix goes through users’ streaming history to predict what they want to watch later.

These innovative technologies help it customize every subscriber’s experience, and produce billions of dollars in revenue. 


Apart from OOT, eCommerce, and retail, the music sector also takes advantage of ML and recommendation engines.

Spotify’s recommender system offers real-time suggestions after looking at the songs skipped by the users, the songs added to the playlist, their favorite artists, and which songs they listen to frequently.

Spotify uses machine learning for recommender systems to send a playlist to 100 Million subscribers every week.

This tailored list consists of approximately 30 fresh songs to encourage their exposure to music. 


It uses an effective recommendation system with complicated algorithms to refine content depending on the user’s age, previous browsing history, search terms, etc. 

YouTube depends mainly on advertisements, so it makes use of an effective recommender system that matches suitable ads according to past data.  

How to Pick the Perfect Product Recommendation Engine?

Picking the perfect product recommendation engine for retail is not an easy task as there are multiple choices obtainable in the market.

So, you have to consider the following features when selecting ML-based recommendation engines for your business:

Relevant recommendations

The system should have strong customization capabilities, and utilize ML algorithms to inspect user interaction with the online shop and recommend products concerning their interests. 

Effortless integration

It’s very important to choose a recommendation technology that can consistently integrate with your eCommerce or retail business.

Make sure that the technology has APIs or plug-ins to facilitate the integration process.

Once the integration is complete, the engine can access and examine user data including search queries, buying behavior, and past browsing details to make tailored suggestions.

Hence, an effortless integration procedure will save valuable time and resources, enabling you to look at different areas of business. 


Business requirements will change as time goes on, so you have to use a system that can act according to those needs.

A scalable system will enable you to monitor multiple products, data, and users without degrading the performance of your business. 

In the majority of cases, retail and eCommerce companies should scale up to billions of SKUs.

That’s why it’s mandatory to examine whether the selected system can manage such heavy amounts of data. 

A cross-sell rules system

Intelligent product recommendations work better if they are consistently put together in various channels. Recommendations that only suit the web or email. 

Powerful collaborative filtering

There are numerous choices for non-customized suggestions.

Ensure your system involves different choices such as “Frequently purchased with this”, “Compare with related items”, and “More from repeatedly purchased brands”.

Indicate fresh products

Alert users of the items that have been updated by producing “There is a fresh version of this product” notifications.

How Do You Create a Product Recommendation Engine?

A powerful product recommendation solution can be challenging to adopt. Below are some easy ways to create a product recommendation engine:

1. Understand customers

The first step is to understand your audience. This involves knowing your audience’s journey, which is divided into 4 steps:

Awareness: An audience has a problem or requirement, but he hasn’t completely defined it.

In this step, provide “top selling items” as suggestions.

Consideration: Soon after defining a problem, an audience thinks of probable solutions

In this step, filter those suggestions to put choices or substitutes to the present solution along with upsells. 

Decision: An audience gets rid of a few solutions to concentrate on a few or the one he will buy

In this step, provide extras to praise the present solution – but stick to their initial decision to make a purchase.   

Validation: After making a purchase, does he feel satisfied with the product?

Re-provide extras and refills and chances for repurchasing.

In case you don’t have enough information on 1st-time consumers, target product suggestions to their step in the purchasing process.

To get a better idea of the consumer journey in detail, you should:

  1. Notice user data
  2. Interviewing consumers
  3. Monitoring traffic paths on the website
  4. Engaging consumers on social platforms
  5. Speaking to people who have direct interactions with consumers
2. Determine products based on relevancy

Soon after inspecting user journeys, the next move is to begin implementing the merchandising rules. 

You consider the following strategies:

Consumer data: Consumer data will be the key to determining the relevancy of a product.

As explained, you can look at the purchase history and past visits of your customers to showcase personalized recommendations.

Here, you will remind consumers that they have saved products in case they added them to the cart but didn’t take action. Also, you will provide them with alternate products to encourage them. 

Moreover, if you have not collected data from customers’ past visits or have less data, you need to utilize data from a fresh visitor’s present session to choose related products. This might involve:

  1. Location of the visitor
  2. Browsing history
  3. Key phrases that led them to your site
  4. Ads that directed them to your site 
  5. Social media activity that led them to your site

Trends: Product trend analysis is the basic foundation for making better product suggestions if you don’t have customized visitor data. 

Here are a few suggestions:

  1. Suggest the best product sellers & most purchased products
  2. Suggest periodic products, but display them at the beginning of the season. As the season progresses, their importance reduces
  3. Suggest discounted products & sales. If you are providing bonus gifts or free shipping options, users love such offers and make purchases even though they were planning to purchase those products in the future.

Page context: It’s very difficult to identify what led potential buyers to your site if they are using a private window or incognito mode in a browser.

In such cases, you have to tailor product suggestions according to which page they are browsing and what you know. 

Homepage: It should have a mixture of trending products and bestsellers.

Category pages: Both trending category items and bestsellers yield better results.

Product pages: Consistently offer identical items that are the right alternatives.

Also, use past data to recommend what others purchase after seeing the product and generate a record of the present session to filter recommendations according to the browsing behavior of users.

Shopping cart pages: Provide extra products instead of alternatives.

3. Selecting the perfect recommendation engine

It’s not bad practice to start with manual product suggestions at the beginning.

Once your brand grows, you start adding more items, and as your visitors grow and diversify, it’s challenging to manage manual product suggestions. 

It’s not easy to implement periodic variability to change shopper trends and behavior. 

Having relevant product recommendation technology is essential to getting the expected upsell outcomes.

Product recommendation systems using machine learning ensure that you can save time and money by providing more quality suggestions.

4. A/B Testing

In the beginning, A/B testing is important to boost confidence that you are using the right recommendation concept. However, you can’t stop testing even after getting more conversions. 

Always be ready with recommendation techniques to execute frequently. They might produce results initially and later fail to get the same results as before.

Test various recommendation strategies consistently, no matter whether there is a drop in sales. 

A few modifications to attempt to involve:

  1. Suggesting various items
  2. Suggesting successful items on various pages
  3. Providing items at various steps of the user journey
  4. Providing various deals or sales
  5. Altering the look or design of the recommendation display
  6. Altering the position of suggestions
  7. Inserting suggestions in fresh places like pop-ups, more pages, and so on
  8. Eliminating rules to grant more control to AI software

As time passes, your product recommendation engine for eCommerce should learn to enhance its recommendations, allowing you to modify a few merchandising rules on the side of automation.

5. Go beyond your brand

Adopt product recommendations into:

  1. Display advertising
  2. Social media marketing
  3. Email marketing 

Product recommendations are a boon for remarketing, as you can utilize them to attract fresh visitors and potential customers.

For instance, geo-targeted display ads can produce better revenue than generic display ads.

Get in touch with us to know how product recommendations drive more revenue

How Do I Measure the Success of Recommendation Engines?

Measuring the effectiveness of a product recommendation engine is challenging but not difficult.

A product recommendation system should increase the below-mentioned metrics:

Variation in site browsing time

A shopper should show more interest in a product feed on the website than a general one.

However, it will not enhance browsing time. Instead, in terms of conversions, browsing time can be reduced. Visitors are getting quick results. 

Increased CTRs on email campaigns

Both triggered and promotional efforts (browse and cart abandonment) could be more engaging with suggestions.

Increased click rates on websites

When the item feed is customized, click rates should increase as shoppers see what you’ve recommended for them. 

Increased open rates for emails

Category details can be used to subject lines to compel customers to open emails with accurate recommendations. 

Higher AOV (average order value)

In case your suggestions are designed to provoke shoppers to purchase items that correspond with the ones they are looking for, the average order value should enhance.  

Increased sales

An increase in sales is a powerful metric, whether from email campaigns or complete retail or eCommerce sales.

How Does Customer Information Management (CIM) Enhance Recommendation Engines?

To deliver personalized experiences, there is a need for high-quality data associated with the visitor.

Information including transaction history, product feedback, customer service records, and browsing records are internally produced and captured to provide detailed insights about the visitor from which new experiences can be extracted.

Business owners use rule-based concepts to overcome the issue associated with data fragmentation.

The drawback of the rule-based concept is, it supports limited scenarios within the limited cluster of rules making business owners unclear about what’s required, and keeping them out of other unspecified possibilities.

These drawbacks led to the introduction of ML algorithms in customer information management (CIM) or customer data platform (CDP), which supports a use case linked with real-time audience data.

In CDP or CIM, machine learning algorithms are adapted to churn out suggestions by combining past, and real-time data of audiences.

Later, store them in a dashboard, and utilized as a response technique for activating campaigns over numerous platforms.

How Express Analytics can Help?

Express Analytics’ AI product recommendation engine assists you in adjusting your recommendations efforts with the audience’s step in the buyer journey or audience lifecycle.

Product recommendations provided by the company merge real-time information with critical merchandising rules and advanced machine learning to highlight the most accurate recommendations for every audience. 

With Express Analytics, both retail and eCommerce companies can develop a recommendation concept that matches their business goals.

With our simple-to-use dashboard, it’s not difficult to A/B test various kinds of recommendations and implement what yields the best results.


Building a powerful product recommendation program is a challenging and continuous task that involves a multidimensional viewpoint and expertise in ethics, machine learning, and data science.

By incorporating innovative concepts and producing ethical frameworks, you can build the best product recommendation engine that matches shopper demands and fosters loyalty and engagement. In the end, productive recommendation systems have the power to change the way you find and engage with products, services, and content.


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